ArticleScientific reports2024
A novel automated label data extraction and data base generation system from herbarium specimen images using OCR and NER.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Robust and accurate multimodal information extraction from tobacco box labels using MLDP.Scientific reports · 2026Article
- Application of Computer Vision to the Automated Extraction of Metadata from Natural History Specimen Labels: A Case Study on Herbarium Specimens.Plants (Basel, Switzerland) · 2026Article
- Automated information extraction from plant specimen labels using OCR and large language models.Biodiversity data journal · 2026Article
- Hespi: a pipeline for automatically detecting information from herbarium specimen sheets.Bioscience · 2025Article
- Streamlining data recording through optical character recognition: a prospective multi-center study in intensive care units.Critical care (London, England) · 2025Observational
- Article
- SpeciMate: Improving metadata extraction from digitised biological specimens.Biodiversity data journal · 2025Article
- The digitisation workflow of the herbarium of the State Museum of Natural History of the NAS of Ukraine (LWS).Biodiversity data journal · 2025Article
- Article
- Ensemble automated approaches for producing high-quality herbarium digital records.Applications in plant sciencesArticle
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Authors and funding
3 authors.
Funding
Abstract
Digital extraction of label data from natural history specimens along with more efficient procedures of data entry and processing is essential for improving documentation and global information availability. Herbaria have made great advances in this direction lately. In this study, using optical character recognition (OCR) and named entity recognition (NER) techniques, we have been able to make further advancements towards fully automatic extraction of label data from herbarium specimen images. This system can be developed and run on a consumer grade desktop computer with standard specifications, and can also be applied to extracting label data from diverse kinds of natural history specimens, such as those in entomological collections. This system can facilitate the digitization and publication of natural history museum specimens around the world.
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